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Applications of Remote Sensing for Terrestrial Ecosystem Biochemical Responses to Climate Change and Drought

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 27288

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Dr. Manuela Balzarolo

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Department of Biology, University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Belgium
Interests: terrestrial remote sensing; ecology; carbon cycle; drought; phenology; climate change
Special Issues, Collections and Topics in MDPI journals

Dr. Frank Veroustraete

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Guest Editor

University of Antwerp, Groenenborgerlaan 171, 2020 Antwerpen, Belgium
Interests: remote sensing of vegetation;ecophysiology of plants; impact assessment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Climate change and climate extremes (e.g., drought) affect the terrestrial carbon balance and ecosystem functionalities. They modify both the rates of carbon uptake by photosynthesis (e.g., gross primary productivity) and release by total ecosystem respiration. Remote sensing has long been used to understand the impact of climate change on the spatial and seasonal variability of carbon and water balance at local and global scales.

Remotely sensed indicators can provide an effective way to obtain real-time conditions of ecosystems and offer a range of spatial and temporal observations on changes in ecosystem structure, function, and services. Remote-sensing indicators differ in their sensitivity to changes in photosynthetic status. However, no consensus has been reached regarding the most suitable indicators for quantifying and modeling the effect of climate change and its extremes on terrestrial carbon and water balance.

This Special Issue of Remote Sensing aims at the publication of both review and original research papers related to the following keyword-indicated research topics:

Remote sensing of climate change;
Remote sensing of climate extremes;
Remote sensing of carbon and water cycles;
Remote sensing of arid ecosystems;
Remote sensing of water limited lands;
Remote sensing, remote sensing of bio-geophysical variables;
Remote sensing of drought.

This Special Issue is open to contributions such as review papers and focus papers presenting strategies, methodologies, or approaches leading to the assimilation of remote sensing products from different platforms (e.g., in situ spectroradiometers, UAV, satellites), whether reflected in the optical range or emitted as fluorescence, far-infrared, or microwave radiation, as well as techniques based on different assimilation of remote sensing and in-situ measurements in ecological models. Data and in situ measuring methods for product validation purposes are also welcome.

Dr. Manuela Balzarolo
Dr. Frank Veroustraete
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Climate change
Climate extremes
Drought
Carbon cycle
Water cycle
Photosynthesis
Light use efficiency models
Ecological models
Satellite data
Field spectroscopy

Published Papers (9 papers)

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Research

16 pages, 5273 KiB

Open AccessArticle

Concurrent Climate Extremes and Impacts on Ecosystems in Southwest China

by Lulu Liu, Yuan Jiang, Jiangbo Gao, Aiqing Feng, Kewei Jiao, Shaohong Wu, Liyuan Zuo, Yuqing Li and Rui Yan

Remote Sens. 2022, 14(7), 1678; https://doi.org/10.3390/rs14071678 - 31 Mar 2022

Cited by 6 | Viewed by 2013

Abstract

Global warming and its associated changes in temperature and precipitation have significantly affected the ecosystem in Southwest China, yet studies that integrate temperature and precipitation changes are inadequate for quantitatively assessing the impacts of extreme events on ecosystems. In this study, the return period of concurrent climate extremes characterized by precipitation deficit and extreme temperature and the spatial and temporal dynamic patterns of their impacts on ecosystems were assessed by using high-precision temperature and precipitation data, as well as NDVI and NPP data collected for the 1985–2015 period. The results show that the 2009 concurrent event had a return period of about 200 years. The return periods of individual climate factors are significantly overestimated or underestimated. Concurrent events significantly reduced the spring and annual Normalized Difference Vegetation Index (NDVI) and net primary productivity (NPP) in Southwest China. The magnitude of the reduction in vegetation greenness and productivity increased with the intensity of concurrent events. Concurrent events beginning in autumn 2009 reduced spring NDVI and NPP by 8.8% and 23%, and annual NDVI and NPP by 2.23% and 7.22%, respectively. Under future climate scenarios, the return period of concurrent events could be significantly shortened, which would have a more severe impact on regional ecosystems. Full article

(This article belongs to the Special Issue Applications of Remote Sensing for Terrestrial Ecosystem Biochemical Responses to Climate Change and Drought)

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19 pages, 7753 KiB

Open AccessArticle

Time-Lag Effect of Vegetation Response to Volumetric Soil Water Content: A Case Study of Guangdong Province, Southern China

by Weijiao Li, Yunpeng Wang, Jingxue Yang and Yujiao Deng

Remote Sens. 2022, 14(6), 1301; https://doi.org/10.3390/rs14061301 - 08 Mar 2022

Cited by 4 | Viewed by 2240

Abstract

The content of soil water affects the physiological activities of vegetation, and the type of vegetation also affects the soil water balance. It is of great significance to study the response of vegetation to soil moisture change, which is helpful for understanding the vulnerability of vegetation for regional and environmental protections. The response of vegetation to soil moisture in Guangdong Province from mid-October 2015 to the end of March 2017 was studied by using cloudy region drought index (CRDI) as the drought index and volumetric soil water content (VSWC) as the soil moisture index to measure the level of water stress on vegetation. Taking the peak and valley positions of CRDI and VSWC as characteristic points, the lag time of vegetation to volumetric soil water content was obtained by judging the difference between the peak and valley positions of the two indexes. The results indicate that the response of vegetation to volumetric soil water content in Guangdong lagged 3.33 periods (9–35 days) on average. When VSWC is sufficient, there is no obvious difference in time-lag between different types of vegetation. However, when VSWC is relatively insufficient, grass shows the fastest response to the change of volumetric soil water content. Both longitude and soil moisture affect the lag time of vegetation. Under the same conditions, the higher the soil humidity is, the longer the lag time is, and the longer the delay time is with the greater longitude. CRDI can reflect the time-lag effect between vegetation and VSWC in Guangdong, indicating it is a sensitive and applicable index for characterizing the time-lag phenomena of vegetation to soil moisture. Full article

(This article belongs to the Special Issue Applications of Remote Sensing for Terrestrial Ecosystem Biochemical Responses to Climate Change and Drought)

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18 pages, 3828 KiB

Open AccessArticle

Spectral-Based Monitoring of Climate Effects on the Inter-Annual Variability of Different Plant Functional Types in Mediterranean Cork Oak Woodlands

by Cristina Soares, João M. N. Silva, Joana Boavida-Portugal and Sofia Cerasoli

Remote Sens. 2022, 14(3), 711; https://doi.org/10.3390/rs14030711 - 02 Feb 2022

Cited by 2 | Viewed by 1815

Abstract

Using remotely sensed data to estimate the biophysical properties of vegetation in woodlands is a challenging task due to their heterogeneous nature. The objective of this study was to assess the biophysical parameters of different vegetation types (cork oak trees, shrubs and herbaceous vegetation) in cork oak woodland through the analysis of temporal trends in spectral vegetation indices (VIs). A seven-year database (from 2011 until 2017) of in situ observations collected with a field spectroradiometer with a monthly basis was used and four VIs were derived, considered as proxies for several biophysical properties of vegetation such as biomass (Normalized Difference Vegetation Index—NDVI); chlorophyll content (MERIS Terrestrial Chlorophyll Index-MTCI), tissue water content (Normalized Difference Water Index—NDWI) and the carotenoid/chlorophyll ratio (Photochemical Reflectance Index—PRI). During the analyzed period, some key meteorological data (precipitation, temperature, relative air humidity and global radiation) were collected for the study site, aggregated at three different time-lags (short period (30 d), medium period (90 d) and hydrological period (HIDR)), and their relationship with VIs was analyzed. The results showed different trends for each vegetation index and vegetation type. In NDVI and NDWI, herbaceous vegetation showed a highly marked seasonal trend, whereas for MTCI, it was the cork oak and Cistus salvifolius, and for PRI, it was Ulex airensis that showed the marked seasonal trend. Shrubs have large differences depending on the species: the shallow-rooted Cistus salvifolius showed a higher seasonal variability than the deep-rooted Ulex airensis. Our results revealed the importance of temperature and precipitation as the main climatic variables influencing VI variability in the four studied vegetation types. This study sets up the relationships between climate and vegetation indices for each vegetation type. Spectral vegetation indices are useful tools for assessing the impact of climate on vegetation, because using these makes it easier to monitor the amount of “greenness”, biomass and water stress of vegetation than assessing the photosynthetic efficiency. Proximal remote sensing measurements are fundamental for the correct use of remote sensing in monitoring complex agroforest ecosystems, largely used to inform policies to improve resilience to drought, particularly in the Mediterranean region. Full article

(This article belongs to the Special Issue Applications of Remote Sensing for Terrestrial Ecosystem Biochemical Responses to Climate Change and Drought)

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20 pages, 75997 KiB

Open AccessArticle

Assessment of Vegetation Dynamics and Ecosystem Resilience in the Context of Climate Change and Drought in the Horn of Africa

by Simon Measho, Baozhang Chen, Petri Pellikka, Lifeng Guo, Huifang Zhang, Diwen Cai, Shaobo Sun, Alphonse Kayiranga, Xiaohong Sun and Mengyu Ge

Remote Sens. 2021, 13(9), 1668; https://doi.org/10.3390/rs13091668 - 25 Apr 2021

Cited by 12 | Viewed by 3714

Abstract

Understanding the response of vegetation and ecosystem resilience to climate variability and drought conditions is essential for ecosystem planning and management. In this study, we assessed the vegetation changes and ecosystem resilience in the Horn of Africa (HOA) since 2000 and detected their drivers based mainly on analysis of the Moderate Resolution Imaging Spectroradiometer (MODIS) products. We found that the annual and seasonal trends of NDVI (Normalized Difference Vegetation Index) generally increased during the last two decades over the Horn of Africa particularly in western parts of Ethiopia and Kenya. The weakest annual and seasonal NDVI trends were observed over the grassland cover and tropical arid agroecological zones. The NDVI variation negatively correlated with Land Surface Temperature (LST) and positively correlated with precipitation at a significant level (p < 0.05) account for 683,197 km² and 533,385 km² area, respectively. The ecosystem Water Use Efficiency (eWUE) showed overall increasing trends with larger values for the grassland biome. The precipitation had the most significant effect on eWUE variation compared to LST and annual SPEI (Standardized Evapotranspiration Index). There were about 54.9% of HOA resilient to drought disturbance, whereas 32.6% was completely not-resilient. The ecosystems in the humid agroecological zones, the cropland, and wetland were slightly not-resilient to severe drought conditions in the region. This study provides useful information for policy makers regarding ecosystem and dryland management in the context of climate change at both national and regional levels. Full article

(This article belongs to the Special Issue Applications of Remote Sensing for Terrestrial Ecosystem Biochemical Responses to Climate Change and Drought)

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17 pages, 3107 KiB

Open AccessArticle

Precipitation Drives the NDVI Distribution on the Tibetan Plateau While High Warming Rates May Intensify Its Ecological Droughts

by Kewei Jiao, Jiangbo Gao and Zhihua Liu

Remote Sens. 2021, 13(7), 1305; https://doi.org/10.3390/rs13071305 - 29 Mar 2021

Cited by 33 | Viewed by 2864

Abstract

Climate change has significantly affected the ecosystem of the Tibetan Plateau. There, temperature rises and altered precipitation patterns have led to notable changes in its vegetation growth processes and vegetation cover features. Yet current research still pays relatively little attention to the regional climatic determinants and response patterns of such vegetation dynamics. In this study, spatial patterns in the response of the normalized difference vegetation index (NDVI) to climate change and its dynamic characteristics during the growing season were examined for the Tibetan Plateau, by using a pixel-scale-based geographically weighted regression (GWR) based on the Global Inventory Modeling and Mapping Studies (GIMMS) NDVI data, as well as data for temperature and moisture indices collected at meteorological stations, for the period 1982–2015. The results show the following. Spatial nonstationary relationships, primarily positive, were found between the NDVI and climatic factors in the Tibetan Plateau. However, warming adversely affected vegetation growth and cover in some arid and semiarid regions of the northeast and west Tibetan Plateau. Additionally, precipitation played a dominant role in the NDVI of the Tibetan Plateau in the largest area (accounting for 39.7% of total area). This suggests that increased moisture conditions considerably facilitated vegetation growth and cover in these regions during the study period. Temperature mainly played a dominant role in the NDVI in some parts of the plateau sub-cold zone and some southeastern regions of the Tibetan Plateau. In particular, the minimum temperature was the dominant driver of NDVI over a larger area than any of the other temperature indices. Furthermore, spatial regressions between NDVI dynamics and climatic variability revealed that a faster warming rate in the arid and semiarid regions impeded vegetation growth through mechanisms such as drought intensification. Moisture variability was found to act as a key factor regulating the extent of vegetation cover on the south Tibetan Plateau. Full article

(This article belongs to the Special Issue Applications of Remote Sensing for Terrestrial Ecosystem Biochemical Responses to Climate Change and Drought)

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18 pages, 6536 KiB

Open AccessArticle

Vegetation Productivity Dynamics in Response to Climate Change and Human Activities under Different Topography and Land Cover in Northeast China

by Hui Li, Hongyan Zhang, Qixin Li, Jianjun Zhao, Xiaoyi Guo, Hong Ying, Guorong Deng, Wu Rihan and Shuling Wang

Remote Sens. 2021, 13(5), 975; https://doi.org/10.3390/rs13050975 - 04 Mar 2021

Cited by 30 | Viewed by 3467

Abstract

Net primary productivity (NPP) is the total amount of organic matter fixed by plants from the atmosphere through photosynthesis and is susceptible to the influences of climate change and human activities. In this study, we employed actual NPP (ANPP), potential NPP (PNPP), and human activity-induced NPP (HNPP) based on the Hurst exponent and statistical analysis to analyze the characteristics of vegetation productivity dynamics and to evaluate the effects of climate and human factors on vegetation productivity in Northeast China (NEC). The increasing trends in ANPP, PNPP, and HNPP accounted for 81.62%, 94.90%, and 89.63% of the total area, respectively, and ANPP in 68.64% of the total area will continue to increase in the future. Climate change played a leading role in vegetation productivity dynamics, which promoted an increase in ANPP in 71.55% of the area, and precipitation was the key climate factor affecting ANPP. The aggravation of human activities, such as increased livestock numbers and intensified agricultural activities, resulted in a decrease in ANPP in the western grasslands, northern Greater Khingan Mountains, and eastern Songnen Plain. In particular, human activities led to a decrease in ANPP in 53.84% of deciduous needleleaf forests. The impact of climate change and human activities varied significantly under different topography, and the percentage of the ANPP increase due to climate change decreased from 71.13% to 53.9% from plains to urgent slopes; however, the percentage of ANPP increase due to human activities increased from 3.44% to 21.74%, and the effect of human activities on the increase of ANPP was more obvious with increasing slope. At different altitudes, the difference in the effect of these two factors was not significant. The results are significant for understanding the factors influencing the vegetation productivity dynamics in NEC and can provide a reference for governments to implement projects to improve the ecosystem. Full article

(This article belongs to the Special Issue Applications of Remote Sensing for Terrestrial Ecosystem Biochemical Responses to Climate Change and Drought)

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21 pages, 3461 KiB

Open AccessArticle

Asymmetry of Daytime and Nighttime Warming in Typical Climatic Zones along the Eastern Coast of China and Its Influence on Vegetation Activities

by Guangxin He and Zhongliang Li

Remote Sens. 2020, 12(21), 3604; https://doi.org/10.3390/rs12213604 - 03 Nov 2020

Cited by 9 | Viewed by 2341

Abstract

In this dissertation, the author adopted the normalized difference vegetation index (NDVI) and meteorological data from 1982 to 2016 of the typical climate zones in coastal areas of China to analyze the influence of daytime and nighttime warming asymmetric changes in different seasons on vegetation activities during the growing season period according to the copula function theory optimized based on Markov chain Monte Carlo (MCMC). The main conclusions are as follows: (1) The seasonal daytime and nighttime warming trends of Guangdong, Jiangsu and Liaoning over the past 35 years were significant, and the daytime and nighttime warming rates were asymmetric. In spring and summer of Guangdong province, the warming rate in the daytime was higher than that at night, while, in autumn, the opposite law was observed. However, the warming rate in the daytime was lower than that at night in Jiangsu and Liaoning provinces. There were latitude differences in diurnal and nocturnal warming rate. (2) The daytime and nighttime warming influences on vegetation showed significant seasonal differences in these three regions. In Guangdong, the influence of nighttime warming on vegetation growth in spring is greater than that in summer, and the influences of daytime warming on vegetation growth from strong to weak were spring, summer and autumn. In Jiangsu, both the influences of daytime and nighttime warming on vegetation growth in summer were less than that in autumn. In Liaoning, both the influences of daytime and nighttime warming on vegetation growth from strong to weak were autumn, spring and summer. (3) In Guangdong, Jiangsu and Liaoning provinces, their maximum temperature (Tmax) and minimum temperature (Tmin) and the joint probability distribution functions of NDVI, all had little effect on NDVI when Tmax and Tmin respectively reached their minimum values, but their influences on NDVI were obvious when Tmax and Tmin respectively reached their maximum values. (4) The smaller the return period, the larger the range of climate factor and NDVI, which has indicated that when the climate factor is certain, the NDVI is more likely to have a smaller return period, and the frequency of NDVI over a certain period is higher. In addition, the larger the climate factor, the greater the return period is and NDVI is less frequent over a certain period of time. This research can help with deep understanding of the dynamic influence of seasonal daytime and nighttime asymmetric warming on the vegetation in typical coastal temperature zones of China under the background of global climate change. Full article

(This article belongs to the Special Issue Applications of Remote Sensing for Terrestrial Ecosystem Biochemical Responses to Climate Change and Drought)

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18 pages, 4363 KiB

Open AccessArticle

Spectral Response Assessment of Moss-Dominated Biological Soil Crust Coverage Under Dry and Wet Conditions

by Xiang Chen, Tao Wang, Shulin Liu, Fei Peng, Wenping Kang, Zichen Guo, Kun Feng, Jia Liu and Atsushi Tsunekawa

Remote Sens. 2020, 12(7), 1158; https://doi.org/10.3390/rs12071158 - 04 Apr 2020

Cited by 7 | Viewed by 3139

Abstract

Biological soil crusts (BSCs) are a major functional vegetation unit, covering extensive parts of drylands worldwide. Therefore, several multispectral indices have been proposed to map the spatial distribution and coverage of BSCs. BSCs are composed of poikilohydric organisms, the activity of which is sensitive to water availability. However, studies on dry and wet BSCs have seldom considered the mixed coverage gradient that is representative of actual field conditions. In this study, in situ spectral data and photographs of 136 pairs of dry and wet plots were collected to determine the influence of moisture conditions on BSC coverage detection. Then, BSC spectral reflectance and continuum removal (CR) reflectance responses to wetting were analyzed. Finally, the responses of four commonly used indices (i.e., normalized difference vegetation index (NDVI); crust index (CI); biological soil crust index (BSCI); and band depth of absorption feature after CR in the red band, (BD_red)), calculated from in situ hyperspectral data resampled to two multispectral data channels (Landsat-8 and Sentinel-2), were compared in dry and wet conditions. The results indicate that: (i) on average, the estimated BSC coverage using red-green-blue (RGB) images is 14.98% higher in wet than in dry conditions (P < 0.001); (ii) CR reflectance features of wet BSCs are more obvious than those of dry BSCs in both red and red-edge bands; and (iii) NDVI, CI, and BSCI for BSC coverage of 0%–60% under dry and wet conditions are close to those of dry and wet bare sand, respectively. NDVI and BD_red cannot separate dead wood and BSC with low coverage. This study demonstrates that low-coverage moss-dominated BSC is not easily detected by the four indices. In the future, remote-sensing data obtained during the rainy season with red and red-edge bands should be considered to detect BSCs. Full article

(This article belongs to the Special Issue Applications of Remote Sensing for Terrestrial Ecosystem Biochemical Responses to Climate Change and Drought)

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29 pages, 9465 KiB

Open AccessFeature PaperArticle

Seasonal Adaptation of the Thermal-Based Two-Source Energy Balance Model for Estimating Evapotranspiration in a Semiarid Tree-Grass Ecosystem

by Vicente Burchard-Levine, Héctor Nieto, David Riaño, Mirco Migliavacca, Tarek S. El-Madany, Oscar Perez-Priego, Arnaud Carrara and M. Pilar Martín

Remote Sens. 2020, 12(6), 904; https://doi.org/10.3390/rs12060904 - 11 Mar 2020

Cited by 23 | Viewed by 3988

Abstract

The thermal-based two-source energy balance (TSEB) model has accurately simulated energy fluxes in a wide range of landscapes with both remote and proximal sensing data. However, tree-grass ecosystems (TGE) have notably complex heterogeneous vegetation mixtures and dynamic phenological characteristics presenting clear challenges to earth observation and modeling methods. Particularly, the TSEB modeling structure assumes a single vegetation source, making it difficult to represent the multiple vegetation layers present in TGEs (i.e., trees and grasses) which have different phenological and structural characteristics. This study evaluates the implementation of TSEB in a TGE located in central Spain and proposes a new strategy to consider the spatial and temporal complexities observed. This was based on sensitivity analyses (SA) conducted on both primary remote sensing inputs (local SA) and model parameters (global SA). The model was subsequently modified considering phenological dynamics in semi-arid TGEs and assuming a dominant vegetation structure and cover (i.e., either grassland or broadleaved trees) for different seasons (TSEB-2S). The adaptation was compared against the default model and evaluated against eddy covariance (EC) flux measurements and lysimeters over the experimental site. TSEB-2S vastly improved over the default TSEB performance decreasing the mean bias and root-mean-square-deviation (RMSD) of latent heat (LE) from 40 and 82 W m⁻² to −4 and 59 W m⁻², respectively during 2015. TSEB-2S was further validated for two other EC towers and for different years (2015, 2016 and 2017) obtaining similar error statistics with RMSD of LE ranging between 57 and 63 W m⁻². The results presented here demonstrate a relatively simple strategy to improve water and energy flux monitoring over a complex and vulnerable landscape, which are often poorly represented through remote sensing models. Full article

(This article belongs to the Special Issue Applications of Remote Sensing for Terrestrial Ecosystem Biochemical Responses to Climate Change and Drought)

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